CN111383050A - Product data integration and analysis method, device and computer readable storage medium - Google Patents
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Abstract
The invention relates to a data processing technology, and discloses a product data integration and analysis method, which comprises the following steps: obtaining sales information of a product order, generating a time stamp according to sales time information contained in the sales information, and obtaining an initial order number according to the time stamp; performing feature extraction on the sales information of the product order to obtain order feature information, adding the order feature information into the initial order number to obtain a standard order number, and naming and storing the product order by using the standard order number to obtain standard order information; and collecting all standard order information to obtain a standard order information set, and performing data analysis on product effects according to the standard order information set. The invention also provides a product data integration and analysis device, electronic equipment and a computer readable storage medium. The invention can solve the problems of low product data integration and analysis efficiency and high cost.
Description
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to a method and an apparatus for integrating and analyzing product data, an electronic device, and a computer-readable storage medium.
Background
With the rise of big data, people have more and more extensive demands on various products in the existing market, but various products in the existing market are full of precious and numerous, and users have no way to choose, so that a product supplier is required to provide reasonable and powerful product data integration and analysis result data. For example, as the myopia rate of primary and secondary school students increases, more and more parents want to wear a cornea remodelling mirror on children, but the cornea remodelling mirror is expensive, and whether and how to select a cornea remodelling mirror with better quality and good effect on the market becomes an increasingly important requirement for parents.
Product data integration and analysis usually require a large amount of product sales condition and product effect data as data support, but most of the current merchants are too confused about management of product sales orders, and an efficient method for integrating the product sales condition, product effect and analyzing the product sales condition is lacking.
Disclosure of Invention
The invention provides a product data integration and analysis method, a product data integration and analysis device, an electronic device and a computer readable storage medium, and mainly aims to provide a simple and efficient data integration and analysis scheme.
In order to achieve the above object, the present invention provides a product data integration and analysis method, which comprises:
obtaining sales information of a product order, generating a time stamp according to sales time information contained in the sales information, and obtaining an initial order number according to the time stamp;
performing feature extraction on the sales information of the product order to obtain order feature information, and adding the order feature information into the initial order number to obtain a standard order number;
naming and storing the product order according to the standard order number to obtain standard order information, and collecting all the standard order information to obtain a standard order information set;
and searching the product orders from the standard order information set through the standard order numbers according to the requirements of the user, analyzing the data of the product effect according to the searched product orders to obtain an analysis result, and outputting the analysis result to the user.
Optionally, the performing feature extraction on the sales information of the product order to obtain order feature information includes:
and inputting the sales information into a pre-trained feature extraction model for feature extraction to obtain the order feature information.
Optionally, the method further comprises training the feature extraction model, wherein the training comprises:
step A: randomly generating training order information and standard order characteristics corresponding to the training order information;
and B: converting the training order information by using the feature extraction model to obtain conversion order features;
and C: and C, comparing and judging the conversion order features and the standard order features, if the difference between the conversion order features and the standard order features is larger than or equal to a preset threshold value, adjusting the parameters of the feature extraction model, and returning to the step B to continue to execute the conversion of the order features.
Step D: and if the difference between the converted order features and the standard order features is smaller than a preset threshold value, obtaining a trained feature extraction model.
Optionally, the comparing and determining the conversion order characteristics and the standard order characteristics includes:
calculating a similarity between the converted order characteristics and the standard order characteristics using the following formula:
Simtopic＝Pearson(TPS,TPT)
wherein, SimtopicSimilarity between the conversion order characteristics and the standard order characteristics; TPTFor the conversion order characteristics, TPSThe standard order characteristics.
Optionally, the analyzing the data of the product effect according to the searched product order to obtain an analysis result includes:
transmitting the standard order information set to a preset data analysis system by using a pre-packaged interface calling module;
and in the data analysis system, performing data analysis on the searched product orders to obtain product characteristic information and/or customer characteristic information, and performing data analysis on the product characteristic information and/or customer characteristic information to obtain the analysis result.
Optionally, the transmitting the standard order information set to a preset data analysis system by using a pre-packaged interface calling module includes:
step a: sending a calling request to a data transmission interface of the data analysis system by using the interface calling module;
step b: receiving an interface state returned by a data transmission interface of the data analysis system according to the call request;
step c: returning to the step a to resend the calling request under the condition that the interface state is calling refusal;
step d: and when the interface state is the condition of calling agreement, calling the interface, and transmitting the standard order information set to the data analysis system.
Optionally, the outputting the analysis result to the user includes:
and outputting the analysis result to the user in a visualized chart form.
In order to solve the above problems, the present invention further provides a product data integration and analysis device, including:
the initial order number generation module is used for acquiring sales information of a product order, generating a timestamp according to sales time information contained in the sales information, and obtaining an initial order number according to the timestamp;
the characteristic extraction module is used for carrying out characteristic extraction on the sales information of the product order to obtain order characteristic information, and adding the order characteristic information into the initial order number to obtain a standard order number;
the standard order information generating module is used for naming and storing the product order according to the standard order number to obtain standard order information, and collecting all the standard order information to obtain a standard order information set;
and the data analysis module is used for searching the product orders from the standard order information set through the standard order numbers according to the requirements of the user, analyzing the data of the product effect according to the searched product orders to obtain an analysis result, and outputting the analysis result to the user.
In order to solve the above problem, the present invention also provides an electronic device, including:
a memory storing at least one instruction; and
and the processor executes the instructions stored in the memory to realize the product data integration and analysis method.
In order to solve the above problem, the present invention further provides a computer-readable storage medium, in which at least one instruction is stored, and the at least one instruction is executed by a processor in an electronic device to implement the product data integration and analysis method according to any one of the above aspects.
According to the method and the device, the sales information of the product order is acquired, the timestamp is generated according to the sales time information contained in the sales information, and the order information is initialized and numbered in the timestamp mode, so that the order can be conveniently and efficiently retrieved and processed in the follow-up process; and performing feature extraction on the sales information of the product order to obtain order feature information, adding the order feature information into the initial order number to obtain a standard order number, extracting the order feature information and adding the order feature information into the order number, so that the calculation memory occupied during subsequent data retrieval and analysis is reduced, meanwhile, the accurate definition of the order information is realized, and the subsequent accurate retrieval and processing are facilitated. And performing data analysis on the product according to the information contained in the searched product order, so that a reliable analysis result can be obtained, and the accurate analysis on the product effect is realized. Therefore, the product data integration and analysis method, the product data integration and analysis device and the computer readable storage medium can realize low-cost and high-efficiency data analysis of products according to order data.
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FIG. 1 is a schematic flow chart illustrating a method for product data integration and analysis according to an embodiment of the present invention;
FIG. 2 is a block diagram of product data integration and analysis provided in accordance with an embodiment of the present invention;
fig. 3 is a schematic diagram of an internal structure of an electronic device according to a product data integration and analysis method provided in an embodiment of the present invention;
the implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The invention provides a product data integration and analysis method. Fig. 1 is a schematic flow chart of a product data integration and analysis method according to an embodiment of the present invention. The method may be performed by an apparatus, which may be implemented by software and/or hardware.
In this embodiment, the product data integration and analysis method includes:
s1, obtaining the sales information of the product order, generating a time stamp according to the sales time information contained in the sales information, and obtaining an initial order number according to the time stamp.
In an embodiment of the present invention, the sales information of the product order may be historical sales information of a certain product, for example, a product order of a certain brand of corneal remodelling mirror currently sold in the market. The sales information for the product order may be obtained directly from the sales database of each dealer. Wherein the sales information may include, but is not limited to: time information of product order generation, product characteristic information, customer characteristics, and the like. Wherein, the product characteristic information can include, for example, the degree information of the cornea plastometer, etc.; and the customer characteristics may include the name, gender, age, etc. of the customer.
Further, the embodiment of the present invention obtains the time information generated by the product order in the sales information, digitalizes the time information, and converts the time information into the time stamp. For example, if the time information generated by the order in the sales information is 1/31/2020, the time information can be converted into the form of "20200131" after being digitized, and the timestamp is obtained.
Further, in the embodiment of the present invention, the initial order number of the product order may be obtained according to the timestamp.
And S2, performing feature extraction on the sales information of the product order to obtain order feature information, and adding the order feature information into the initial order number to obtain a standard order number.
Further, the initial order number only contains time information of the order, and order characteristic information is not added, so that in order to make subsequent query on the order faster and more efficient, other characteristic information of the order needs to be further added to the initial order number.
In detail, in the embodiment of the present invention, a preset feature extraction model is used to perform feature extraction from the sales information of the product order, so as to obtain order feature information.
Preferably, the preset feature extraction model is a pre-trained convolutional neural network with a feature extraction function, and can extract order feature information in the sales information of the product order.
Further, the embodiment of the present invention further includes training the feature extraction model, where the training process includes:
step A: randomly generating training order information and standard order characteristics corresponding to the training order information;
and B: converting the training order information by using the feature extraction model to obtain conversion order features;
and C: and C, comparing and judging the conversion order features and the standard order features, if the difference between the conversion order features and the standard order features is larger than or equal to a preset threshold value, adjusting the parameters of the feature extraction model, returning to the step B, and continuing to perform conversion of the order features.
Step D: and if the difference between the converted order features and the standard order features is smaller than a preset threshold value, finishing the training to obtain a trained feature extraction model.
Further, the present invention calculates the difference between the converted order characteristics and the standard order characteristics using the following formula:
Simtopic＝Pearson(TPS,TPT)
wherein, SimtopicA difference between said conversion order characteristics and said standard order characteristics; TPTFor the conversion order characteristics, TPSThe standard order characteristics.
And S3, naming and storing the product order according to the standard order number to obtain standard order information, and collecting all the standard order information to obtain a standard order information set.
Further, naming the corresponding order according to the standard order number, and storing the order in a pre-constructed order database to obtain the standard order information after naming all the orders, so that the standard order information can be quickly called when data analysis is carried out on the order subsequently.
The pre-built order database may be a mysql database or an Oracle database for storing order information.
Further, after the standard order information is completely stored, all the standard order information is collected to obtain the standard order information set.
And S4, searching the product orders from the standard order information set through the standard order numbers according to the requirements of the user, analyzing the data of the product effect according to the searched product orders to obtain an analysis result, and outputting the analysis result to the user.
Further, the embodiment of the present invention performs a search for product orders from the standard order information set transmitted to the data analysis system according to user requirements.
For example, a historical order of the same client is retrieved, and an analysis result of the corneal power of the client on the correction condition actually is obtained according to the power change of the corneal power of the same brand purchased in the historical order each time; or analyzing the types of cornea molding mirror purchase of the clients of different age groups through historical orders of the clients of different age groups to obtain the preference degrees of the clients of different age groups for different types of cornea molding mirrors and the like.
In detail, the searching of the product order from the standard order information set according to the requirement of the user, and performing data analysis of the product effect on the product according to the product order obtained by the searching to obtain an analysis result includes:
and searching standard order information needing to be analyzed in the standard order information set through the standard order number according to the requirements of the user by using an index technology, for example, searching all historical corneal remodelling mirror orders of the same client.
In detail, when the index technology is used for retrieving the standard order information needing to be analyzed in the standard order information set, the embodiment of the invention can retrieve the orders of the same customer in the standard order information by using the modes of number index, keyword index or index algorithm and the like according to the standard order number; and searching the order information of the sections of different ages or other required query sections in the standard order information according to the section index mode.
The embodiment of the invention analyzes the data of the retrieved standard order information by using a preset data analysis program in the data analysis system, such as a data processing program written by using a python language, so as to obtain an analysis result.
In detail, the analyzing the data of the product effect according to the searched product order to obtain an analysis result includes:
transmitting the standard order information set to a preset data analysis system by using a pre-packaged interface calling module;
and in the data analysis system, performing data analysis on the searched product orders to obtain product characteristic information and/or customer characteristic information, and performing data analysis on the product characteristic information and/or customer characteristic information to obtain the analysis result.
Further, the transmitting the standard order information set to a preset data analysis system by using a pre-packaged interface calling module includes:
step a: sending a calling request to a data transmission interface of the data analysis system by using the interface calling module;
step b: receiving an interface state returned by a data transmission interface of the data analysis system according to the call request;
step c: returning to the step a to resend the calling request under the condition that the interface state is calling refusal;
step d: and when the interface state is the condition of calling agreement, calling the interface, and transmitting the standard order information set to the data analysis system.
The embodiment of the invention calls a data transmission interface of a data analysis system by using a pre-packaged interface calling module HttpRestoperations, and transmits the standard order information set to the data analysis system. The bottom layer of the HTTP operations interface is an external HTTP interface called by restTemplate, and the HTTP interface can be used for calling an interface of a database to realize data transmission.
The data analysis system can be a database which has a data analysis function and is used for storing big data, can be used for instantly storing the obtained standard order information, ensures that the standard order information is not lost, also has a data analysis pair function for the standard order information, and can retrieve corresponding product orders according to needs and analyze product effects according to the retrieved product orders.
Further, the data included in the retrieved order information may not be comparable, and the order information needs to be converted according to the following formula:
wherein x' is the standardized order information, x is the order information, and x ismaxIs the largest information data, x, in the order informationminAnd the order information is the minimum information data.
Further, in the embodiment of the present invention, the standardized order information is subjected to data analysis by using a data analysis program provided in the data analysis system to obtain an analysis result, a data analysis algorithm written in python language and built in the data analysis system may be used to perform data analysis, and the following weight algorithm may be used to calculate a ratio weight of a purchased target keratoplasty mirror in a plurality of standard order information of a certain age group:
wherein b is the order number of the target orthokeratology lens in the plurality of standard order information of the certain age group, and a is the total order number of the age group.
Through the calculation of the algorithm, the proportion weight of the target orthokeratology lens in the age group can be obtained, so that the preference degree of the age group to different orthokeratology lens pairs can be obtained.
Further, after the data analysis is completed, the embodiment of the present invention may display and output the analysis result in a visualized form, for example, display the analysis result in an excle table, or a histogram, a pie chart, or the like.
According to the method and the device, the sales information of the product order is acquired, the timestamp is generated according to the sales time information contained in the sales information, and the order information is initialized and numbered in the timestamp mode, so that the order can be conveniently and efficiently retrieved and processed in the follow-up process; and performing feature extraction on the sales information of the product order to obtain order feature information, adding the order feature information into the initial order number to obtain a standard order number, extracting the order feature information and adding the order feature information into the order number, so that the calculation memory occupied during subsequent data retrieval and analysis is reduced, meanwhile, the accurate definition of the order information is realized, and the subsequent accurate retrieval and processing are facilitated. And performing data analysis on the product according to the information contained in the searched product order, so that a reliable analysis result can be obtained, and the accurate analysis on the product effect is realized. Therefore, the product data integration and analysis method, the product data integration and analysis device and the computer readable storage medium can realize low-cost and high-efficiency data analysis of products according to order data.
FIG. 2 is a functional block diagram of the product data integration and analysis apparatus according to the present invention.
The product data integration and analysis device 100 of the present invention can be installed in an electronic device. According to the realized functions, the product data integration and analysis device may include an initial order number generation module 101, a feature extraction module 102, a standard order information generation module 103, and a data analysis module 104. A module according to the present invention, which may also be referred to as a unit, refers to a series of computer program segments that can be executed by a processor of an electronic device and that can perform a fixed function, and that are stored in a memory of the electronic device.
In the present embodiment, the functions regarding the respective modules/units are as follows:
the initial order number generation module 101 is configured to obtain sales information of a product order, generate a timestamp according to sales time information included in the sales information, and obtain an initial order number according to the timestamp.
In an embodiment of the present invention, the sales information of the product order may be historical sales information of a certain product, for example, a product order of a certain brand of corneal remodelling mirror currently sold in the market. The sales information for the product order may be obtained directly from the sales database of each dealer. Wherein the sales information may include, but is not limited to: time information of product order generation, product characteristic information, customer characteristics, and the like. Wherein, the product characteristic information can include, for example, the degree information of the cornea plastometer, etc.; and the customer characteristics may include the name, gender, age, etc. of the customer.
Further, the embodiment of the present invention obtains the time information generated by the product order in the sales information, digitalizes the time information, and converts the time information into the time stamp. For example, if the time information generated by the order in the sales information is 1/31/2020, the time information can be converted into the form of "20200131" after being digitized, and the timestamp is obtained.
Further, in the embodiment of the present invention, the initial order number of the product order may be obtained according to the timestamp.
The feature extraction module 102 is configured to perform feature extraction on the sales information of the product order to obtain order feature information, and add the order feature information to the initial order number to obtain a standard order number.
Further, the initial order number only contains time information of the order, and order characteristic information is not added, so that in order to make subsequent query on the order faster and more efficient, other characteristic information of the order needs to be further added to the initial order number.
In detail, in the embodiment of the present invention, a preset feature extraction model is used to perform feature extraction from the sales information of the product order, so as to obtain order feature information.
Preferably, the preset feature extraction model is a pre-trained convolutional neural network with a feature extraction function, and can extract order feature information in the sales information of the product order.
Further, the embodiment of the present invention further includes training the feature extraction model, where the training process includes:
step A: randomly generating training order information and standard order characteristics corresponding to the training order information;
and B: converting the training order information by using the feature extraction model to obtain conversion order features;
and C: and C, comparing and judging the conversion order features and the standard order features, if the difference between the conversion order features and the standard order features is larger than or equal to a preset threshold value, adjusting the parameters of the feature extraction model, returning to the step B, and continuing to perform conversion of the order features.
Step D: and if the difference between the converted order features and the standard order features is smaller than a preset threshold value, finishing the training to obtain a trained feature extraction model.
Further, the present invention calculates the difference between the converted order characteristics and the standard order characteristics using the following formula:
Simtopic＝Pearson(TPS,TPT)
wherein, SimtopicA difference between said conversion order characteristics and said standard order characteristics; TPTFor the conversion order characteristics, TPSThe standard order characteristics.
The standard order information generating module 103 is configured to name and store the product order according to the standard order number to obtain standard order information, and collect all standard order information to obtain a standard order information set.
Further, naming the corresponding order according to the standard order number, and storing the order in a pre-constructed order database to obtain the standard order information after naming all the orders, so that the standard order information can be quickly called when data analysis is carried out on the order subsequently.
The pre-built order database may be a mysql database or an Oracle database for storing order information.
Further, after the standard order information is completely stored, all the standard order information is collected to obtain the standard order information set.
The data analysis module 104 is configured to search the product order from the standard order information set through the standard order number according to a requirement of a user, perform data analysis on a product effect according to the searched product order, obtain an analysis result, and output the analysis result to the user.
Further, the embodiment of the present invention performs a search for product orders from the standard order information set transmitted to the data analysis system according to user requirements.
For example, a historical order of the same client is retrieved, and an analysis result of the corneal power of the client on the correction condition actually is obtained according to the power change of the corneal power of the same brand purchased in the historical order each time; or analyzing the types of cornea molding mirror purchase of the clients of different age groups through historical orders of the clients of different age groups to obtain the preference degrees of the clients of different age groups for different types of cornea molding mirrors and the like.
In detail, the searching of the product order from the standard order information set according to the requirement of the user, and performing data analysis of the product effect on the product according to the product order obtained by the searching to obtain an analysis result includes:
and searching standard order information needing to be analyzed in the standard order information set through the standard order number according to the requirements of the user by using an index technology, for example, searching all historical corneal remodelling mirror orders of the same client.
In detail, when the index technology is used for retrieving the standard order information needing to be analyzed in the standard order information set, the embodiment of the invention can retrieve the orders of the same customer in the standard order information by using the modes of number index, keyword index or index algorithm and the like according to the standard order number; and searching the order information of the sections of different ages or other required query sections in the standard order information according to the section index mode.
The embodiment of the invention analyzes the data of the retrieved standard order information by using a preset data analysis program in the data analysis system, such as a data processing program written by using a python language, so as to obtain an analysis result.
In detail, the analyzing the data of the product effect according to the searched product order to obtain an analysis result includes:
transmitting the standard order information set to a preset data analysis system by using a pre-packaged interface calling module;
and in the data analysis system, performing data analysis on the searched product orders to obtain product characteristic information and/or customer characteristic information, and performing data analysis on the product characteristic information and/or customer characteristic information to obtain the analysis result.
Further, the transmitting the standard order information set to a preset data analysis system by using a pre-packaged interface calling module includes:
step a: sending a calling request to a data transmission interface of the data analysis system by using the interface calling module;
step b: receiving an interface state returned by a data transmission interface of the data analysis system according to the call request;
step c: returning to the step a to resend the calling request under the condition that the interface state is calling refusal;
step d: and when the interface state is the condition of calling agreement, calling the interface, and transmitting the standard order information set to the data analysis system.
The embodiment of the invention calls a data transmission interface of a data analysis system by using a pre-packaged interface calling module HttpRestoperations, and transmits the standard order information set to the data analysis system. The bottom layer of the HTTP operations interface is an external HTTP interface called by restTemplate, and the HTTP interface can be used for calling an interface of a database to realize data transmission.
The data analysis system can be a database which has a data analysis function and is used for storing big data, can be used for instantly storing the obtained standard order information, ensures that the standard order information is not lost, also has a data analysis pair function for the standard order information, and can retrieve corresponding product orders according to needs and analyze product effects according to the retrieved product orders.
Further, the data included in the retrieved order information may not be comparable, and the order information needs to be converted according to the following formula:
wherein x' is the standardized order information, x is the order information, and x ismaxIs the largest information data, x, in the order informationminAnd the order information is the minimum information data.
Further, in the embodiment of the present invention, the standardized order information is subjected to data analysis by using a data analysis program provided in the data analysis system to obtain an analysis result, a data analysis algorithm written in python language and built in the data analysis system may be used to perform data analysis, and the following weight algorithm may be used to calculate a ratio weight of a purchased target keratoplasty mirror in a plurality of standard order information of a certain age group:
wherein b is the order number of the target orthokeratology lens in the plurality of standard order information of the certain age group, and a is the total order number of the age group.
Through the calculation of the algorithm, the proportion weight of the target orthokeratology lens in the age group can be obtained, so that the preference degree of the age group to different orthokeratology lens pairs can be obtained.
Further, after the data analysis is completed, the embodiment of the present invention may display and output the analysis result in a visualized form, for example, display the analysis result in an excle table, or a histogram, a pie chart, or the like.
Fig. 3 is a schematic structural diagram of an electronic device for implementing the product data integration and analysis method according to the present invention.
The electronic device 1 may comprise a processor 10, a memory 11 and a bus, and may further comprise a computer program, such as a product data integration and analysis program 12, stored in the memory 11 and executable on the processor 10.
The memory 11 includes at least one type of readable storage medium, which includes flash memory, removable hard disk, multimedia card, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disk, optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device 1, such as a removable hard disk of the electronic device 1. The memory 11 may also be an external storage device of the electronic device 1 in other embodiments, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the electronic device 1. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device 1. The memory 11 may be used not only to store application software installed in the electronic device 1 and various types of data, such as codes of the product data integration and analysis program 12, but also to temporarily store data that has been output or is to be output.
The processor 10 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same or different functions, including one or more Central Processing Units (CPUs), microprocessors, digital Processing chips, graphics processors, and combinations of various control chips. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects various components of the electronic device by using various interfaces and lines, and executes various functions and processes data of the electronic device 1 by running or executing programs or modules (for example, executing product data integration and analysis programs and the like) stored in the memory 11 and calling data stored in the memory 11.
The bus may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is arranged to enable connection communication between the memory 11 and at least one processor 10 or the like.
According to the method and the device, the sales information of the product order is acquired, the timestamp is generated according to the sales time information contained in the sales information, and the order information is initialized and numbered in the timestamp mode, so that the order can be conveniently and efficiently retrieved and processed in the follow-up process; and performing feature extraction on the sales information of the product order to obtain order feature information, adding the order feature information into the initial order number to obtain a standard order number, extracting the order feature information and adding the order feature information into the order number, so that the calculation memory occupied during subsequent data retrieval and analysis is reduced, meanwhile, the accurate definition of the order information is realized, and the subsequent accurate retrieval and processing are facilitated. And performing data analysis on the product according to the information contained in the searched product order, so that a reliable analysis result can be obtained, and the accurate analysis on the product effect is realized. Therefore, the product data integration and analysis method, the product data integration and analysis device and the computer readable storage medium can realize low-cost and high-efficiency data analysis of products according to order data.
Fig. 3 shows only an electronic device with components, and it will be understood by those skilled in the art that the structure shown in fig. 3 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than those shown, or some components may be combined, or a different arrangement of components.
For example, although not shown, the electronic device 1 may further include a power supply (such as a battery) for supplying power to each component, and preferably, the power supply may be logically connected to the at least one processor 10 through a power management device, so as to implement functions of charge management, discharge management, power consumption management, and the like through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The electronic device 1 may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.
Further, the electronic device 1 may further include a network interface, and optionally, the network interface may include a wired interface and/or a wireless interface (such as a WI-FI interface, a bluetooth interface, etc.), which are generally used for establishing a communication connection between the electronic device 1 and other electronic devices.
Optionally, the electronic device 1 may further comprise a user interface, which may be a Display (Display), an input unit (such as a Keyboard), and optionally a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable for displaying information processed in the electronic device 1 and for displaying a visualized user interface, among other things.
It is to be understood that the described embodiments are for purposes of illustration only and that the scope of the appended claims is not limited to such structures.
The product data integration and analysis program 12 stored in the memory 11 of the electronic device 1 is a combination of instructions that, when executed in the processor 10, can implement:
storing the acquired file and generating a file task list;
calling the files in the file task list to be collected as a temporary file set, and classifying the files in the temporary file set according to keywords;
selecting files from the temporary file set according to the classification and placing the selected files into a preset task queue;
and writing the files in the task queue into preset electronic equipment. Specifically, the specific implementation method of the processor 10 for the instruction may refer to the description of the relevant steps in the embodiment corresponding to fig. 1, which is not described herein again.
Further, the integrated modules/units of the electronic device 1, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. The computer-readable medium may include: any entity or device capable of carrying said computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM).
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.
The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any accompanying claims should not be construed as limiting the claim concerned.
Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the system claims may also be implemented by one unit or means in software or hardware. The terms second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (10)
1. A method for product data integration and analysis, the method comprising:
obtaining sales information of a product order, generating a time stamp according to sales time information contained in the sales information, and obtaining an initial order number according to the time stamp;
performing feature extraction on the sales information of the product order to obtain order feature information, and adding the order feature information into the initial order number to obtain a standard order number;
naming and storing the product order according to the standard order number to obtain standard order information, and collecting all the standard order information to obtain a standard order information set;
and searching the product orders from the standard order information set through the standard order numbers according to the requirements of the user, analyzing the data of the product effect according to the searched product orders to obtain an analysis result, and outputting the analysis result to the user.
2. The product data integration and analysis method of claim 1, wherein the performing feature extraction on the sales information of the product order to obtain order feature information comprises:
inputting the sales information into a pre-trained feature extraction model for feature extraction to obtain the order feature information.
3. The method of product data integration and analysis of claim 2, further comprising training the feature extraction model, wherein the training comprises:
step A: randomly generating training order information and standard order characteristics corresponding to the training order information;
and B: converting the training order information by using the feature extraction model to obtain conversion order features;
and C: comparing and judging the conversion order features and the standard order features, if the difference between the conversion order features and the standard order features is larger than or equal to a preset threshold value, adjusting the parameters of the feature extraction model, and returning to the step B to continue to perform conversion of the order features;
step D: and if the difference between the converted order features and the standard order features is smaller than a preset threshold value, obtaining a trained feature extraction model.
4. The product data integration and analysis method of claim 3, wherein said comparing said conversion order characteristics to said standard order characteristics comprises:
calculating a similarity between the converted order characteristics and the standard order characteristics using the following formula:
Simtopic＝Pearson(TPS,TPT)
wherein, SimtopicSimilarity between the conversion order characteristics and the standard order characteristics; TPTFor the conversion order characteristics, TPsThe standard order characteristics.
5. The method for product data integration and analysis according to claim 1, wherein the analyzing the data of product effect according to the searched product order to obtain the analysis result comprises:
transmitting the standard order information set to a preset data analysis system by using a pre-packaged interface calling module;
and in the data analysis system, performing data analysis on the searched product orders to obtain product characteristic information and/or customer characteristic information, and performing data analysis on the product characteristic information and/or customer characteristic information to obtain the analysis result.
6. The method for product data integration and analysis according to claim 5, wherein the step of transmitting the standard order information set to a predetermined data analysis system using a pre-packaged interface calling module comprises:
step a: sending a calling request to a data transmission interface of the data analysis system by using the interface calling module;
step b: receiving an interface state returned by a data transmission interface of the data analysis system according to the call request;
step c: returning to the step a to resend the calling request under the condition that the interface state is calling refusal;
step d: and when the interface state is the condition of calling agreement, calling the interface, and transmitting the standard order information set to the data analysis system.
7. The product data integration and analysis method of any one of claims 1 to 6, wherein the outputting the analysis result to the user comprises:
and outputting the analysis result to the user in a visualized chart form.
8. A product data integration and analysis device, the device comprising:
the initial order number generation module is used for acquiring sales information of a product order, generating a timestamp according to sales time information contained in the sales information, and obtaining an initial order number according to the timestamp;
the characteristic extraction module is used for carrying out characteristic extraction on the sales information of the product order to obtain order characteristic information, and adding the order characteristic information into the initial order number to obtain a standard order number;
the standard order information generating module is used for naming and storing the product order according to the standard order number to obtain standard order information, and collecting all the standard order information to obtain a standard order information set;
and the data analysis module is used for searching the product orders from the standard order information set through the standard order numbers according to the requirements of the user, analyzing the data of the product effect according to the searched product orders to obtain an analysis result, and outputting the analysis result to the user.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the product data integration and analysis method of any of claims 1 to 7.
10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the product data integration and analysis method according to any one of claims 1 to 7.
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